{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Company Contributions\n",
    "\n",
    "* This notebook attempts to compute number of contributions (currently just PRs) by various companies\n",
    "* It relies on github_users.json to map users to affiliation\n",
    "\n",
    "  * **github_users.json** Is outdated and needs to be updated\n",
    "  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# NOTE: The RuntimeWarnings (if any) are harmless. See ContinuumIO/anaconda-issues#6678.\n",
    "from pandas.io import gbq\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import getpass\n",
    "import subprocess\n",
    "# Configuration Variables. Modify as desired.\n",
    "\n",
    "PROJECT = subprocess.check_output([\"gcloud\", \"config\", \"get-value\", \"project\"]).strip().decode()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setup Authorization\n",
    "\n",
    "If you are using a service account run\n",
    "%%bash\n",
    "\n",
    "# Activate Service Account provided by Kubeflow.\n",
    "gcloud auth activate-service-account --key-file=${GOOGLE_APPLICATION_CREDENTIALS}\n",
    "\n",
    "If you are running using user credentials\n",
    "\n",
    "gcloud auth application-default login"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "import datetime\n",
    "month = datetime.datetime.now().month\n",
    "year = datetime.datetime.now().year\n",
    "\n",
    "num_months = 12\n",
    "\n",
    "months = []\n",
    "for i in range(num_months):\n",
    "    months.append(\"\\\"{0}{1:02}\\\"\".format(year, month))\n",
    "    month -= 1\n",
    "    if month == 0:\n",
    "        month = 12\n",
    "        year -=1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Read in user affiliations\n",
    "\n",
    "* github_users.json is produced using CNCF scripts\n",
    "* There can be multiple entries for a user showing their company & affiliation during different time periods"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "import os\n",
    "import requests\n",
    "if not os.path.exists(\".cache\"):\n",
    "    os.makedirs(\".cache\")\n",
    "    \n",
    "\n",
    "users_file = os.path.join(\".cache\", \"github_users.json\")\n",
    "\n",
    "if not os.path.exists(users_file):\n",
    "    url = \"https://github.com/kubeflow/community/blob/master/devstats/data/github_users.json?raw=true\"\n",
    "\n",
    "    r = requests.get(url, allow_redirects=True)\n",
    "    \n",
    "    with open(users_file, \"wb\") as hf:\n",
    "        hf.write(r.content)\n",
    "\n",
    "with open(users_file) as hf:    \n",
    "    data = json.load(hf)\n",
    "users=pd.DataFrame(data)\n",
    "users = users[[\"login\", \"company\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dedupe companies\n",
    "c = [\"cisco\", \"datawire\", \"google\", \"ibm\", \"intel\", \"teradata\", \"red hat\"]\n",
    "known_companies = dict(zip(c,c))\n",
    "known_companies[\"redhat\"] = \"red hat\"\n",
    "def normalize_company(name):\n",
    "    if name is None:\n",
    "        return \"None\"\n",
    "    name = name.strip().lower().strip(\"!\").strip(\"@\")\n",
    "        \n",
    "     \n",
    "    for k, v in known_companies.items():\n",
    "        if k in name:\n",
    "            return v\n",
    "    return name\n",
    "\n",
    "users[\"company\"] = users[\"company\"].apply(normalize_company)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Users can have multiple entries\n",
    "* We pick the first non None entry\n",
    "* TODO(jlewi) We should find a better way to combine multiple entries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'google'"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def combine_company(names):\n",
    "    for i in names:\n",
    "        if i != \"None\":\n",
    "            return i\n",
    "    return None\n",
    "\n",
    "user_map= users.groupby(\"login\")[\"company\"].apply(combine_company)\n",
    "\n",
    "# You can now look up users as user_map[actor]\n",
    "user_map[\"jlewi\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Attribute PRs to Companies"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* We use BigQuery to get a list of PRs\n",
    "* We then map each PR to the company of its author"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/jovyan/.local/lib/python3.6/site-packages/pandas_gbq/gbq.py:555: UserWarning: A progress bar was requested, but there was an error loading the tqdm library. Please install tqdm to use the progress bar functionality.\n",
      "  progress_bar_type=progress_bar_type,\n"
     ]
    }
   ],
   "source": [
    "query = \"\"\"\n",
    "SELECT\n",
    "    DATE(created_at) AS pr_date,\n",
    "    actor.id,\n",
    "    actor.login\n",
    "  FROM `githubarchive.month.*`\n",
    "  WHERE\n",
    "    _TABLE_SUFFIX IN ({0})\n",
    "    AND type = 'PullRequestEvent'\n",
    "    AND org.login = 'kubeflow'\n",
    "    AND JSON_EXTRACT(payload, '$.action') IN ('\"opened\"')\n",
    "\"\"\".format(\",\".join(months))\n",
    "\n",
    "prs=gbq.read_gbq(str(query), dialect='standard', project_id=PROJECT)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "p=pd.Series(data=prs[\"id\"].values,index=prs[\"pr_date\"])\n",
    "p=p.sort_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pr_date</th>\n",
       "      <th>id</th>\n",
       "      <th>login</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2019-05-01</td>\n",
       "      <td>4784270</td>\n",
       "      <td>gabrielwen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2019-05-01</td>\n",
       "      <td>4739316</td>\n",
       "      <td>Jeffwan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2019-05-01</td>\n",
       "      <td>1497445</td>\n",
       "      <td>derekhh</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2019-05-01</td>\n",
       "      <td>3724388</td>\n",
       "      <td>cheyang</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2019-05-02</td>\n",
       "      <td>3631320</td>\n",
       "      <td>animeshsingh</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5986</th>\n",
       "      <td>2019-06-23</td>\n",
       "      <td>146453</td>\n",
       "      <td>kkasravi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5987</th>\n",
       "      <td>2019-07-03</td>\n",
       "      <td>2348602</td>\n",
       "      <td>IronPan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5988</th>\n",
       "      <td>2020-01-16</td>\n",
       "      <td>38020283</td>\n",
       "      <td>kubeflow-bot</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5989</th>\n",
       "      <td>2020-01-16</td>\n",
       "      <td>37601826</td>\n",
       "      <td>kunmingg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5990</th>\n",
       "      <td>2020-01-16</td>\n",
       "      <td>20940800</td>\n",
       "      <td>MATRIX4284</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5991 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        pr_date        id         login\n",
       "0    2019-05-01   4784270    gabrielwen\n",
       "1    2019-05-01   4739316       Jeffwan\n",
       "2    2019-05-01   1497445       derekhh\n",
       "3    2019-05-01   3724388       cheyang\n",
       "4    2019-05-02   3631320  animeshsingh\n",
       "...         ...       ...           ...\n",
       "5986 2019-06-23    146453      kkasravi\n",
       "5987 2019-07-03   2348602       IronPan\n",
       "5988 2020-01-16  38020283  kubeflow-bot\n",
       "5989 2020-01-16  37601826      kunmingg\n",
       "5990 2020-01-16  20940800    MATRIX4284\n",
       "\n",
       "[5991 rows x 3 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "prs[\"company\"] = user_map[prs[\"login\"]].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \n"
     ]
    }
   ],
   "source": [
    "d=prs[[\"pr_date\", \"company\"]]\n",
    "d[\"count\"]=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "pr_counts = d.pivot_table(\"count\", columns=\"company\", index=\"pr_date\", aggfunc=\"sum\", fill_value=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Some solutions here: https://stackoverflow.com/questions/46470743/how-to-efficiently-compute-a-rolling-unique-count-in-a-pandas-time-series\n",
    "# Need to figure out how to do a time based window\n",
    "\n",
    "counts = pr_counts.rolling('28d').sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Make a plot\n",
    "\n",
    "* Plot a subset of companies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ]
    },
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       "</script>"
      ],
      "text/plain": [
       "alt.Chart(...)"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "names = [\"google\", \"cisco\", \"microsoft\", \"arrikto\", \"ibm\", \"seldon\"]\n",
    "companies_df = counts[names]\n",
    "companies_df[\"day\"] = companies_df.index\n",
    "companies = pd.melt(companies_df, value_vars=names, id_vars=[\"day\"])\n",
    "\n",
    "chart = alt.Chart(companies, title= \"PRs\")\n",
    "point = chart.mark_point().encode(\n",
    "  x= alt.X('day', title = \"Day\"),\n",
    "  y=alt.Y(\"value\", title=\"# PRs\"),\n",
    "  color=\"company\",\n",
    ")\n",
    "\n",
    "point.interactive()"
   ]
  }
 ],
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